Vehicle guidance systems known in the art support the driver by providing longitudinal guidance of the vehicle (acceleration and deceleration) and/or during transverse guidance (tracking, steering). The functions of such guidance systems range from simple speed regulation to a desired speed selectable by the driver via adaptive cruise control (ACC) in which the distances to preceding vehicles are also taken into consideration, to completely autonomous vehicle guidance. Further examples of functions of such a vehicle guidance system include the automatic generation of collision warnings or the automatic introduction of emergency braking or evasive maneuvers to avoid or minimize the effects of collisions. The traffic situation is determined using sensors attached to the vehicle, the signals of which are supplied as input quantities to a control unit. The input quantities relate to the motion values of the guided vehicle itself, e.g., its driving speed, acceleration, yaw velocity, and the like, as well as to information regarding the traffic environment, in particular location data for preceding vehicles and other obstacles as well as any applicable information regarding the road course, the road condition, and the like. One or more distance sensors are typically provided for acquiring location data, e.g., a radar sensor for measuring distances and relative speeds of radar targets, in the case of a radar sensor having angular resolution also for measuring the azimuth angle of the radar targets, or lidar sensors, or camera systems, in particular stereo camera systems having electronic image processing. The control unit uses the input quantities supplied by these sensors to calculate control variables that act on the vehicle via control elements of the drive system and in some instances also of the braking system. An example of an ACC system of this type is described in SAE paper no. 96 10 10, “Adaptive Cruise Control System, Aspects and Development Trends”, Winner et al., 1996.
The calculation of the control variables by the control unit depends on a plurality of parameters, of which several may be changed dynamically even during the control operation as a function of the traffic situation.
For example, during radar-supported distance control, a plurality of objects are typically located at the same time by the radar system. An object list is then created in which the individual objects are represented by their distance, relative speed, and angle data. During the periodically repeated radar measurements, a tracking procedure is used to identify the objects detected in the current measurement via the objects detected in previous measurements, and the movements of the individual objects are tracked. Since according to distance control a vehicle directly ahead in the same lane is to be followed at a suitable safety distance, a parameter is required that specifies which of the plurality of objects is to be selected as the target object for the distance control. This parameter should be adjusted to the particular traffic situation on the basis of suitable criteria.
In practice, the input signals transmitted from the sensors to the control unit are more or less noise-infested and must therefore be processed using a suitable filter. Each of these filter procedures is influenced by one or more parameters that determine the temporal resolution of the filter, e.g., integration times, decay rates, or the selection of frequency ranges in the frequency spectrum of the signal. The filters must each be parameterized such that sufficient noise and interfering signal suppression is achieved and also that input quantity changes are transmitted with sufficient speed to allow for timely reaction of the guidance system.
Predictive controllers that extrapolate the movements of the own vehicle and those of the located objects for the future, thereby predicting the traffic situation for a future point in time are often used in the control unit. The control variables are then calculated such that an optimum adjustment to the predicted traffic situation is achieved within a certain optimization time interval. Longer prediction time periods and optimization time intervals lead to “predictive” performance of the guidance system and as such to significant driving comfort but have the disadvantage that the probability of false forecasts increases and suddenly occurring changes may not be reacted to appropriately in some instances. Therefore, these parameters are also to be suitably determined.
Furthermore, it must be decided when predicting the future development of an input quantity, e.g., the distance from an object, whether linear extrapolation is to be performed under the assumption that the relative speed remains constant, or quadratic extrapolation under the assumption of constant acceleration, or an extrapolation of an even higher order. In some instances, in the case of sudden state changes, e.g., during an abrupt braking maneuver of the preceding vehicle, plausible assumptions must also be made in the prediction as to how long this state will last.
In general, the determination of the parameters that determine the behavior of the different control functions of the control unit require an evaluation of the traffic situation. To date, either the measured or derived kinetic state quantities of the own vehicle and the detected objects, e.g., the speed of the own vehicle, the distance and the relative speed of the preceding vehicle, etc., or simple quantities derived from these state quantities, e.g., the time to collision (TTC), i.e., the calculated time to impact, have been used as the situation-specific quantities for this evaluation.